Global Exponential Stability of Delayed Neural Networks Based on a New Integral Inequality

This paper focuses on the problem of exponential stability for a class of neural networks with time-varying delays. A more general inequality is established which extends the auxiliary function-based integral inequality. Based on the inequality and parameter-dependent matrix inequality, an improved delaydependent stability criterion is obtained by constructing an augmented Lyapunov functional. Three numerical examples are given to illustrate the efficiency of the method.